• DeepSeek Reports Theoretical 545% Profit Margins: Chinese AI startup DeepSeek disclosed that its V3 and R1 models achieved a theoretical profit margin of 545% over a 24-hour period, highlighting the efficiency of their inference optimization methods.

• EA Releases Source Code for Command & Conquer Games: Electronic Arts has open-sourced the code for four Command & Conquer titles under the GPL license, allowing the community to explore and modify these classic games.

• Perplexity Integrates GPT-4.5: Perplexity AI has incorporated GPT-4.5 into its platform, offering Pro users up to 10 queries daily with the advanced language model.

• Anthropic Enhances Claude 3.7: Anthropic has improved the tool usage of its Claude 3.7 model, achieving up to a 70% reduction in token usage, thereby enhancing efficiency.

• LangChain Introduces LangGraph Swarm: LangChain has unveiled LangGraph Swarm, a Python library designed for building multi-agent systems with dynamic collaboration capabilities.

• How to define Cursor AI behavior.

• Oculus co-founder Brendan Iribe's new startup, Sesame, has introduced the Conversational Speech Model (CSM), which enhances real-time conversational speech by adjusting tone, pace, and rhythm based on context and emotions.

• Chegg sues Google over AI overviews causing loss of traffic and harming sudents access to quality education.

Urban Intelligence and AI and Ecological Design in Metropolitan Evolution

The Ecological Metropolis Movement

Contemporary urban centers are undergoing a metamorphosis through the fusion of computational intelligence and ecological stewardship. OPEN-TEC, with backing from TCC TECHNOLOGY GROUP, examines the intersection where algorithmic systems, sensor networks, and cutting-edge computational frameworks catalyze resource conservation, energy optimization, and urban habitat enhancement.

Ecological Imperatives in Urban Transformation

Harmonizing metropolitan blueprints with global sustainability frameworks represents a cornerstone of modern urban development. Exemplary cities have emerged as pioneers: Copenhagen's ambitious carbon-neutrality aspiration by 2025 stands alongside Singapore's comprehensive Green Plan 2030, which champions thermally efficient structures and water conservation systems. Environmental accreditations establish benchmarks for ecologically conscious construction, exemplified by One Bangkok's pursuit of LEED-ND Platinum recognition through sophisticated energy orchestration and verdant communal spaces.

Cognitive Computing in Urban Ecosystems

Artificial intelligence elevates urban administration through data-centric governance, refining energy allocation, building system automation, and environmental surveillance. Networked sensors yield instantaneous metrics on atmospheric conditions and power utilization patterns. Seoul's responsive waste receptacles illustrate this approach, minimizing transportation emissions by alerting collection personnel only when vessels reach capacity.

Partnerships and Hurdles

Thriving metropolitan intelligence demands cross-sector collaboration between commercial entities, research institutions, and professional coalitions. Predominant obstacles include the absence of unified global protocols and the seamless integration of algorithmic systems with legacy infrastructure. Educational initiatives and research endeavors prove essential in addressing technical disparities and establishing uniform methodological standards.

Horizon Perspectives

Despite implementation complexities, computationally enhanced urban environments present extensive possibilities—from heightened energy performance to enhanced quality of life. Algorithm-powered applications bolster transit coordination, civic security, and resource distribution. Metropolitan centers like Bangkok, Singapore, Copenhagen, and Seoul demonstrate how computational frameworks can simultaneously elevate residential satisfaction while advancing ecological preservation.

As population centers evolve, the computational augmentation of urban environments remains pivotal to achieving an interconnected, ecologically viable future.

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Claude 3.7 Sonnet: Anthropic's First Extended Thinking Model

The Latest in AI: Anthropic's February Announcement

On Monday, February 24th, 2025, Anthropic unveiled Claude 3.7 Sonnet, marking a significant milestone as their first model explicitly trained to leverage additional inference time tokens for enhanced performance. Alongside this release, they introduced Claude Code as a limited research preview—a command line tool designed for "agentic coding." While not revolutionary, this release represents a meaningful step forward in Anthropic's AI development journey.

Performance That Leads the Pack

Claude 3.7 Sonnet demonstrates clear improvements over its predecessor, Claude 3.5 Sonnet, continuing to excel in areas where users have come to appreciate Claude's capabilities. The model achieves state-of-the-art scores in software development (as measured by SWE Bench) and tool use applications.

Notably, Claude 3.7 Sonnet has claimed the top position as the highest-scoring "standard non-reasoning" language model on the Aider Polyglot benchmark. This achievement is particularly impressive considering it integrates high-level coding capabilities without requiring extra inference time compute—effectively bringing down the cost of superhuman coding assistance.

The Extended Thinking Advantage

What sets Anthropic's approach apart is their commitment to a single, unified model design. Unlike OpenAI's "model dropdown disaster," Claude 3.7 Sonnet offers extended thinking capabilities within the same model—similar to xAI's Grok 3 and what OpenAI has suggested will be their approach with GPT-5.

This single-model strategy makes infrastructure, product, and training decisions more streamlined, though it may come at the cost of some performance optimization opportunities. The reasoning capabilities feel more integrated with the model's standard behavior, rather than feeling like a completely separate system.

A More Transparent User Experience

Claude 3.7 Sonnet displays its reasoning traces directly to users, following a trend also seen with DeepSeek R1 and Grok 3. This transparency helps build user trust, while Anthropic uses these reasoning traces internally to monitor the model's alignment.

Interestingly, Anthropic notes they "didn't perform standard character training on the model's thought process," revealing a more authentic view into how Claude thinks "out of the box." This provides a fascinating glimpse into the model's internal representations.

Developer Controls for Precise Thinking

For developers, Claude 3.7 Sonnet offers a cleaner interface with explicit control over the thinking process. Developers can request a specific number of "thinking tokens" before the model shifts to generating answer tokens—all within a single autoregressive stream.

This explicit control represents a growing focus on managing the reasoning phases in AI models. Rather than relying on prompt engineering to induce extended thinking, developers can now directly tune these settings to optimize for their specific use cases.

Parallel Paths to Better Answers

While not yet implemented in the current release, Anthropic researchers are exploring parallel test-time computation—generating multiple independent thought processes and selecting the best one without prior knowledge of the correct answer.

Approaches include majority voting and using a second language model (or another copy of Claude) to evaluate the work. Early experiments with the challenging AIME math evaluation show promising results, with performance scaling in relation to the number of parallel solutions generated.

The Industry Context

Claude 3.7 Sonnet reinforces a broader industry trend toward making AI more usable rather than just more intelligent. As progress in scaling single-stream performance begins to slow, the natural next frontier appears to be parallel processing and verification methods.

The release highlights how reinforcement learning training offers a path to leveraging inference time scaling laws, though longer-term solutions will likely involve more diverse approaches to managing inference-time tradeoffs.

While Claude 3.7 Sonnet doesn't represent a step change for Claude or the industry, it continues Anthropic's steady march forward. Small, incremental improvements like these are expected to accumulate massively throughout 2025, ultimately transforming what's possible with AI assistance.

As infrastructure challenges remain the primary limiting factor for serving these advanced models to users, Anthropic's focus on a clean, unified approach positions them well for the next phase of AI development.